Amin Hassani

Learn More
In this paper, we study the effect of collaboration between nodes for direction of arrival (DOA) estimation in a full connected wireless acoustic sensor network (WASN) where the position of the nodes is unknown. Each node is equipped with a linear microphone array which defines a node-specific DOA with respect to a single common target speech source. We(More)
In this paper, we present a distributed algorithm for network-wide signal subspace estimation in a fully-connected wireless sensor network with multi-sensor nodes. We consider scenarios where the noise field is spatially correlated between the nodes. Therefore, rather than an eigenvalue decomposition (EVD-) based approach, we apply a generalized EVD (GEVD-)(More)
We compare and contrast the approaches and key features of two proposals for fault-tolerant MPI: User-Level Failure Mitigation (UFLM) and Fault-Aware MPI (FA-MPI). We show how they are complementary and also how they could leverage each other through modifications and/or extensions. We show how to "weaken" and extend ULFM to help integrate it with FA-MPI,(More)
The linearly constrained minimum variance (LCMV) beam-former has been widely employed to extract (a mixture of) multiple desired speech signals from a collection of microphone signals, which are also polluted by other interfering speech signals and noise components. In many practical applications , the LCMV beamformer requires that the subspace(More)
MPI is insufficient when confronting failures. FA-MPI (Fault-Aware MPI) provides extensions to the MPI standard designed to enable data-parallel applications to achieve resilience without sacrificing scalability. FA-MPI introduces transactions as a novel extension to the MPI message-passing model. Transactions support failure detection, isolation,(More)